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476 results for “footprints”
DArTseq genetic dataset associated with the article "Hybrids as mirrors of the past: genomic footprints reveal spatio-temporal dynamics and extinction risk of alpine extremophytes in the mountains of Central Asia"
<p>Description: This file stores genetic information on the single nucleotide polymorphism markers (SNPs) in the examined alkali grasses (Poaceae: Puccinellia). The dataset was generetad by Genome-Wide Restriction Fragment Analysis via the DArTseq platform (Diversity Arrays Technology Pty Ltd, Canberra, Australia), which combines complexity reduction methods, fragment size selection, and high-throughput sequencing, optimised for a target organism. The file contains raw data.<br> <br>Usage notes: We used R (version 4.2.2, 2022-10-31; https://www.R-project.org/) and RStudio (version 2022.07.2+576 "Spotted Wakerobin" Release (e7373ef832b49b2a9b88162cfe7eac5f22c40b34, 2022-09-06; http://www.rstudio.com/) on Windows 8.1 to handle this file. We used the dartR R-package (version 2.7.2) with necessary dependencies to import, proccess and analyse this data file as an object of a class genlight (dartR) in the R environment. You may also handle the file as an object of a class genlight using the adegenet and ade4 R-packages. To learn more about installation procedure and how to use of the R-packages visit: https://cran.r-project.org/web/packages/available_packages_by_name.html.<br> </p>
Database with characterization factors for material flow accounting (material footprint) for process-based LCA for ecoinvent 3.7.1 and 3.8 in openLCA
<p>This dataset contains extend material-specific material footprint indicators (raw material input RMI and total material requirement TMR) compiled by Mostert and Bringezu (2019) to process-based LCA in form of the databases ecoinvent 3.7.1 and 3.8 (Wernet et al. 2016) with the openLCA software. The scope of the material flows covered here largely fits to the materials list used for the Mineral Resource Scarcity indicator of the ReCiPe life cycle impact assessment (LCIA) method (Huijbregts et al. 2016).</p>
Reference Datasets for: SHAFTS (v2022.3): a deep-learning-based Python package for Simultaneous extraction of building Height And FootprinT from Sentinel Imagery
<p>These are reference building height and footprint datasets which consist of 46 cities worldwide and support the development of SHAFTS (https://github.com/LllC-mmd/3DBuildingInfoMap).</p> <p>The snapshot of original reference datasets from 46 cities and related GitHub repository has been created as a zipped file named <strong><em>SHAFTS_220527_snapshot.zip</em></strong>.</p> <p>On 2022.5.27, we added 8 additional cities from ArcGIS Hub when compared with the previous version (https://doi.org/10.5281/zenodo.6370003).</p>
The cosmic carbon footprint of massive stars stripped in binary systems
<p># Structure</p> <p>## Overview</p> <p>The folder data/ contains the inlists and mod files used in this work. Intermediate data (like history or profile) files must be regenerated from the provided files.</p> <p>The folder plots/ contains a jupyter notebook set-up to remake all plots (assuming the data is saved in the data/ folder). There are also additional scripts and files needed to reproduce this work.</p> <p>The griffith.txt file is the data from https://ui.adsabs.harvard.edu/abs/2021arXiv210309837G/abstract and was accessed from https://github.com/giganano/VICE/blob/master/vice/yields/ccsne/S16/W18F/FeH0/v0/explosive/c.dat</p> <p><br> ## Data folders</p> <p>corehedep - Evolution from ZAMS to end of core helium burning<br> coreodep - Evolution from end of core helium burning to end of core oxygen burning<br> cc - Evolution from end of core oxygen burning up to core collapse<br> ccsn - Evolution from core collapse to shock breakout</p> <p>engmc - Tests variations in the injection energy and mass cut of ccsn explosions<br> spacetime - Test variations in space/time resolution of ccsn explosions<br> ccsn_t_m - Test variations in injection time and injection mass of ccsn explosions</p> <p>## Sub-folders</p> <p>corehedep/base - Base folder with inlists for this set of models<br> corehedep/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> corehedep/single - Contains a folder for each mass for the single stars (11-45)<br> corehedep/net/23 - A single star 23msun model ran with a larger nuclear network</p> <p>coreodep/base - Base folder with inlists for this set of models<br> coreodep/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> coreodep/single - Contains a folder for each mass for the single stars (11-45)</p> <p>coreodep/mesh - Test variations with respect to space and time during carbon burning<br> coreodep/overshoot - Test variations with respect to overshoot during carbon burning<br> coreodep/net - A single star 23msun model ran with a larger nuclear network</p> <p>cc/base - Base folder with inlists for this set of models<br> cc/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> cc/single - Contains a folder for each mass for the single stars (11-45)</p> <p><br> ccsn/base - Base folder with inlists for this set of models<br> ccsn/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> ccsn/single - Contains a folder for each mass for the single stars (11-45)<br> ccsn/laplace_binary - Contains core collapse explosions of the binary-stripped models from Laplace et al 2021<br> ccsn/laplace_single - Contains core collapse explosions of the single star models from Laplace et al 2021</p> <p>spacetime/base - Base folder with inlists for this set of models<br> spacetime/binary - Test variations in space/time resolution of ccsn explosions</p> <p>ccsn_t_m/base - Base folder with inlists for this set of models<br> ccsn_t_m/binary - Test variations in injection time and injection mass of ccsn explosions</p> <p>engmc/base - Base folder with inlists for this set of models<br> engmc/binary - Tests variations in the injection energy and mass cut of ccsn explosions</p> <p><br> ## Notes</p> <p>Folders with the name '_k' have enhanced profile output for use in Kippenhan plots.</p> <p>Folders with the name '_v' have enhanced profile output for use in the video of the shock explosion.</p> <p>Each numbered folder contains a set of inlists used (which usually only vary one or two parameters), the rest of the inlists are stored in the base/ folders (See the submit.sh files for how to get MESA to read these files). They also contain a initial.mod file (which is the starting point for this phase of evolution), this is a softlink to the final.mod file from the previous phase (thus coreodep soft links to files in corehedep, corehdep uses MESA's built in ZAMS models to start).</p> <p>The folders that handle the core collapse explosions have additional .mod files that handle each phase of the explosion. See the base/ folders for details on the order.</p> <p>## Files</p> <p>cacheHist.py - Runs mesaplot code to turn history files into a python binary file for faster reading.<br> plotKip.py - Does a quick kippenhan plot for diagnostics</p> <p> </p>
Replication data for: Demographic declines and responses of breeding bird populations to human footprint in the Athabasca Oil Sands Region, Alberta, Canada
<p class="MsoNormal">This data package includes data files and an R script to reproduce results reported in the paper "Demographic declines and responses of breeding bird populations to human footprint in the Athabasca Oil Sands Region, Alberta, Canada". Analyses include hierarchical multispecies models applied to data from 31 bird species at 38 Monitoring Avian Productivity and Survivorship (MAPS) stations to assess 10-year (2011–2020) demographic trends and responses to energy sector disturbance (human footprint proportion) in the Athabasca oil sands region of Alberta, Canada. Adult captures, productivity, and residency probability all declined over the study period, and adult apparent survival probability also tended to decline. Trends in adult captures, productivity, and survival were all more negative at stations with larger increases in disturbance over the study period. Species associated with early seral stages were more commonly captured at more disturbed stations, while species typical of mature forests were more commonly captured at less disturbed stations. Productivity was positively correlated with disturbance within 5 km of stations after controlling for disturbance within 1 km of stations. Adult apparent survival showed relatively little response to disturbance; stresses experienced beyond the breeding grounds likely play a larger role in influencing survival. Residency probability was negatively related to disturbance within 1-km scale of stations and could reflect processes affecting the ability of birds to establish or maintain territories in disturbed landscapes.</p>
P. maxima footprints from J-K14 quadrates
**Description:**<br> **Location**: Site 1 of Ipolytarnóc locality, Hungary<br> **Position**: J-K14 quadrates of site 1<br> **Age**: Lower Miocene<br> **Material**: A trackway includes (from the bottom up) right pes (J-K14/1), left manus (J-K14/2) and left pes (J-K14/3)<br> **Reference**: Gábor Botfalvai, János Magyar, Veronika Watah, Imre Szarvas & Péter Szolyák (2022): Large-sized pentadactyl carnivore footprints from the early Miocene fossil track site at Ipolytarnóc (Hungary): 3D data presentation and ichnotaxonomical revision, Historical Biology,<br> DOI: 10.1080/08912963.2022.2109967<br> Source: Objaverse 1.0 / Sketchfab
P. maxima footprint from C18 quadrate
**Description:**<br> **Location**: Site 1 of Ipolytarnóc locality, Hungary<br> **Position**: C18 quadrate of site 1<br> **Age**: Lower Miocene<br> **Material**: Imprint of left pes<br> **Reference**: Gábor Botfalvai, János Magyar, Veronika Watah, Imre Szarvas & Péter Szolyák (2022): Large-sized pentadactyl carnivore footprints from the early Miocene fossil track site at Ipolytarnóc (Hungary): 3D data presentation and ichnotaxonomical revision, Historical Biology,<br> DOI: 10.1080/08912963.2022.2109967<br> Source: Objaverse 1.0 / Sketchfab
Replica of the holotype footprint of P. maxima
**Description:** <br> 3D model of a copy of the Platykopus maxima holotype. <br> **Location**: Ipolytarnóc locality, Hungary<br> **Position**: Unknown<br> **Age**: Lower Miocene<br> **Material**: The holotype specimen (V 2022.1.1) of the P. maxima is housed in the SZTFH Collection (Budapest, Hungary). Imprint of left manus (Inventory number SZTFH V 2022.1.1)<br> **Reference**: Gábor Botfalvai, János Magyar, Veronika Watah, Imre Szarvas & Péter Szolyák (2022): Large-sized pentadactyl carnivore footprints from the early Miocene fossil track site at Ipolytarnóc (Hungary): 3D data presentation and ichnotaxonomical revision, Historical Biology,<br> DOI: 10.1080/08912963.2022.2109967<br> Source: Objaverse 1.0 / Sketchfab
Uncertainty sensitivity estimates for EXIOBASE 3.8.2 footprints
<p>This data set provides uncertainty sensitivity estimates for EXIOBASE 3.8.2 footprints. Sensitivity levels of footprints of all EXIOBASE regions/extensions were calculated using Linear Error propagation (assuming a 0.1 relative standard deviation) such as that each of the 49 EXIOBASE regions has 124 footprint sensitivity estimates and a total of 6076 sensitivity estimates. This dataset was calculated as part of the paper "<em>Uncertainty propagation in EE-MRIO footprint estimates</em>" Badr & Stadler (2024).</p> <p>We reccoment interpreting footprint sensititivity levels as: 0-0.02: low sensitivity, 0.02-0.03: medium sensitivity, 0.03-0.04: Highly sensitive, 0.04 or more: Extremely sensitive. </p> <p>The paper repository can be found on: gitlab.com/hitea/variance-in-uncertainties-in-mrios</p>
Fig. 5 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 5: Spatial and temporal distribution of Gobius niger in the Marchica Lagoon.
Fig. 1 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 1: Map of the study area.
ETHOS.BUILDA: Building Footprint and Height Dataset Germany
<h2>Introduction</h2> <p>This dataset contains all buildings in Germany with their footprint polygon and height. It is a partial dump of the ETHOS.BUILDA database (version v7_20240429). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset. </p> <p>ETHOS.BUILDA is made available under the <a href="http://opendatacommons.org/licenses/odbl/1.0" target="_blank" rel="noopener">Open Database License (ODbL)</a>. The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.</p> <h2>Data structure and file overview</h2> <p>Building data is provided per federal state, the files are named according to the <a href="https://en.wikipedia.org/wiki/NUTS_statistical_regions_of_Germany" target="_blank" rel="noopener">NUTS-1</a> region names. The building data has the following fields:</p> <table> <tbody> <tr> <td><strong>field name</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ID</td> <td>unique identifier of the building</td> </tr> <tr> <td>source</td> <td>the source of the building footprint</td> </tr> <tr> <td>footprint</td> <td>footprint polygon in WKT-format, EPSG:3035</td> </tr> <tr> <td>height_m</td> <td> <p>value: height of the building in [m], </p> <p>source: source of the height data,</p> <p>lineage: height assignment method</p> </td> </tr> </tbody> </table> <p>A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no source entry for the source "v7_model.json" in the sources.csv file, as this refers to the internally trained machine learning model and not to an external dataset.</p> <h2>Acknowledgements</h2> <p>This work was supported by the Helmholtz Association under the program "Energy System Design". </p> <p>Furthermore, the authors would like to express their gratitude to the Federal Ministry for Economic Affairs and Climate Action (BMWK.IIB4) for providing the necessary resources to conduct this study. Our research was supported by the WAAGE Grant Program (Grant No. 03EI1044/03EE 5031D), and we appreciate their financial assistance.</p>
E-storage driven sustainable and resilient city renaissance with lifecycle carbon footprints and levelized costs of carbon abatement
<p>The dataset contains: The simulation results of the energy balance data, battery capacity sizing data and battery degradation data. The data set also includes calculations and results based on the net present value, carbon emission, levelized cost of storage (LCOS), levelized cost of energy (LCOE) and levelized cost of carbon abatement (LCCA).</p>
Fig. 6 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 6: Scatterplot matrices for Large (A) and Linear (B) FM functions.
Fig. 8 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 8: High values of the FP c index as estimated by Local Moran's I test.
Fig. 5 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 5: Spatial representation of coastal vessels activity indexes (Ac).
Fig. 4 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 4: Spatial representation of the Coastal fishery suitability index (Sc).
Fig. 3 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 3: Spatial representation of the criteria ranking taken into account in MCDA.
Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization (DataSet)
<p>Data from the article: <br>"Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization", L. Reyes, K. Campos, G. D. Avendaño, L. González-Paz, A. Vivas, Y. J. Alvarado, and S. Flores.</p> <p>Data to be used with some implementation of the forecasting method of reference:<br>Sugihara G. and May R. M., Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series, <em>Nature</em> <strong>344</strong>, 734–741 (1990).</p>
Figure 3 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 3. Flow diagram for calculation of WF in Van province.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.